.sits_distances_sample

<p>Distances associated to a time series.</p>

distances

frac

<p>Takes a sits tibble with different labels and
             returns a new tibble. For a given field as a group criterion,
             this new table contains a given number or percentage
             of the total number of samples per group. Parameter n indicates
             the number of random samples with reposition.
             Parameter frac indicates a fraction of random samples
             without reposition. If frac &gt; 1, no sampling is done.</p>

internal

A set of tools for working with satellite image time series, both as individual data or as part of data cubes.
Builds data cubes from collections in AWS, Brazil Data Cube, and Digital Earth Africa.
Provides different visualisation methods for image time series.
Provides smoothing methods for noisy time series.
Includes functions for smoothing based on Bayesian inference.
Enables different clustering methods, including dendrograms and self-organized maps (SOM).
Matches noiseless patterns with noisy time series using the time-weighted dynamic time warping (TWDTW) method for shape recognition.
Provides machine learning methods for time series classification, including support vector machines (SVM), random forests and deep learning.
Minimum recommended requirements: 8 GB RAM and 1 CPU dual-core.

Gilberto Camara

sits

Satellite Image Time Series Analysis for Remote Sensing Data Cubes

Rolf Simoes

Felipe Souza

Lorena Santos

Alexandre Carvalho

Pedro Andrade

Karine Ferreira

Alber Sanchez

Gilberto Queiroz

.sits_distances_sample function

Sample a percentage of a time series distance matrix — .sits_distances_sample

Sample a percentage of a time series distance matrix

.sits_distances_sample: Sample a percentage of a time series distance matrix

Description

Usage

Arguments

Value